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Neither the current nor the next ISO/IEC standard of the Ada programming language does support memory model aware programming on language level. Instead one may employ compiler intrinsics for writing memory model aware software. This results in programs that are less comprehensible than what generally is expected from Ada programs. To remedy this facts we have implemented a precompiler, called AdaMM, which allows a programmer to use the language additions introduced in previous papers and produces Ada code equipped with GCC compiler intrinsics. Studying examples, we compare input and output of AdaMM on the basis of several static software engineering metrics. In addition, we give arguments why memory model aware programming should be supported on higher language level.

AEiC 2021 - 25th Ada-Europe International Conference on Reliable Software Technologies

A precompiler for memory model aware programming on higher language level

AAMAS (International Conference on Autonomous Agents and Multiagent Systems) is the largest and most influential conference in the area of agents and multiagent systems. The aim of the conference is to bring together researchers and practitioners in all areas of agent technology and to provide a single, high-profile, internationally renowned forum for research in the theory and practice of autonomous agents and multiagent systems. AAMAS is the flagship conference of the non-profit International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS).

The AAMAS conference series was initiated in 2002 in Bologna, Italy as a joint event comprising the 6th International Conference on Autonomous Agents (AA), the 5th International Conference on Multiagent Systems (ICMAS), and the 9th International Workshop on Agent Theories, Architectures, and Languages (ATAL).

Subsequent AAMAS conferences have been held in Melbourne, Australia (July 2003), New York City, NY, USA (July 2004), Utrecht, The Netherlands (July 2005), Hakodate, Japan (May 2006), Honolulu, Hawaii, USA (May 2007), Estoril, Portugal (May 2008), Budapest, Hungary (May 2009), Toronto, Canada (May 2010), Taipei, Taiwan (May 2011), Valencia, Spain (June 2012), Minnesota, USA (May 2013), Paris, France (May 2014), Istanbul, Turkey (May 2015), Singapore (May 2016), São Paulo (2017) and Stockholm, Sweden (2018), Montreal (May 2019), Auckland (May 2020, Virtual), London (May 2021, Virtual).
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AAMAS 2022

AAMAS (International Conference on Autonomous Agents and Multiagent Systems) is the largest and most influential conference in the area of agents and multiagent systems. The aim of the conference is to bring together researchers and practitioners in all areas of agent technology and to provide a single, high-profile, internationally renowned forum for research in the theory and practice of autonomous agents and multiagent systems. AAMAS is the flagship conference of the non-profit International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS).

technical paper

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### Welcome Message from our Co-Chairs

Through this message, we want to give you our warmest welcome to the 25th Ada-Europe International Conference on Reliable Software Technologies (AEiC 2021). The special circumstances we all go through have made the conference a virtual event. Nonetheless, this will allow us to take advantage of the benefits of this format by using the Underline.io platform that allows remote participation of speakers and attendees and a virtual exhibition.
The platform will also provide a virtual space through Gather.Town to facilitate social interaction among participants, which, in addition to presenting state of the art research results, is one of the most important goals of a conference. This platform will hold various social events together with breaks and a poster session. 

The conference schedule includes a four-day program and exhibition from Monday to Thursday, with parallel tutorials on Monday, and keynotes, technical sessions and a Work-in-Progress session on the remaining three days. The technical sessions include the presentations of 13 journal-track papers, 5 industrial presentations and 6 invited talks on leading topics on reliable software technologies. The program will be completed with three outstanding keynotes that will delight the participants.

The main conference activities will be from 13:00 to 18:00 Central European Summer Time (CEST) to allow participants from America to Central Asia to attend to the conference sessions. Virtual social events are scheduled at the beginning and the end of each day. We encourage you to participate in these activities as well as visiting the exhibition. We look forward to meeting you virtually in June 2021!

**J. Javier Gutiérrez and Mario Aldea Rivas**
 // AEiC 2021 Program Co-Chairs

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Autonomous systems execute complex tasks to perceive the environment and take self-aware decisions with limited human interaction. This autonomy is commonly achieved with the support of Machine Learning algorithms that provide the system with the ability to learn from sensor data. The nature of these algorithms, that need to process large data volumes, poses high-performance demands on the underlying hardware. As a result, the embedded critical real-time domain is adopting increasingly powerful processors that combine multi-core processors with accelerators such as GPUs. The resulting hardware and software complexity makes it difficult to demonstrate that the system will run safely and reliably. This paper tackles part of this challenge by introducing best programming practices and diagnostic mechanisms on the matrix-matrix multiplication function, a central element of existing Machine Learning libraries, in accordance to the recommendations of functional safety standards such as IEC 61508 and ISO 26262. We then evaluate a selection of diagnostic techniques applied to the matrix-matrix multiplication software in the context of integration and execution in high performance multi-core devices. As a result, we provide a catalogue of diagnostic mechanisms with varying degrees of diagnostic coverage for matrix-matrix multiplication execution on heterogeneous high-performance architectures, so that safety engineers can choose the best option for their needs. Moreover, we assess the diagnostic mechanisms on one of the ARM R5 cores of a Zynq UltraScale+ Multi-Processor System-on-Chip and on a Intel i7 processor with native code employing vectorization for the sake of performance.

Towards functional safety compliance of matrix-matrix multiplication for machine learning-based autonomous systems.

The goal of Industry 4.0 is to be faster, more efficient and more customer-centric, by enhancing the automation and digitalisation of production systems. Frequently, the production in Industry 4.0 is categorized as safety-critical, for example, due to the interactions between autonomous machines and hazardous substances that can result in human injury or death, damage to machines, property or the environment. In order to demonstrate the acceptable safety of production operations, safety cases are constructed to provide comprehensive, logical and defensible justification of the safety of a production system for a given application in a predefined operating environment. However, the construction and maintenance of safety cases in alignment with Industry 4.0 are challenging tasks. For their construction, besides the modular, dynamic and reconfigurable nature of Industry 4.0, the architectural levels of the things, fog and cloud computing have to be considered. The safety cases constructed at system design and development phases might be invalidated during production operations, thus necessitating some means for dynamic safety assurance. Moreover, flexible manufacturing in Industry 4.0 also underlines the need for safety assurance in a dynamic manner during the operational phase. Currently published studies are not explicitly supporting the safety assurance of Industry 4.0, which is the focus of this paper with special emphasis on dynamic safety assurance. At first, the Hazard and Operability (HAZOP) and Fault Tree Analysis (FTA) techniques are used for the identification and mitigation/elimination of potential hazards. Next, based on the hazard analysis results, we derived the safety requirements and safety contracts. Subsequently, safety cases are constructed using the OpenCert platform and safety contracts are associated with them to enable necessary changes during runtime. Finally, we use a simulations based approach to identify and resolve the deviations between the system understanding reflected in the safety cases and the current system operation. The dynamic safety assurance is demonstrated using a use case scenario of materials transportation and data flow in the Industry 4.0 context.

Towards dynamic safety assurance for Industry 4.0

Automated Guided Vehicles (AGVs) are widely used for materials transportation. Operating them in a platooned manner has the potential to improve safety, security and efficiency, control overall traffic flow and reduce resource usage. However, the published studies on platooning focus mainly on the design of technical solutions in the context of automotive domain. In this paper we focus on a largely unexplored theme of platooning in production sites transformed to the Industry 4.0, with the aim of providing safety and security assurances. We present an overall approach for a fault- and threat tolerant platooning for materials transportation in production environments. Our functional use cases include the platoon control for collision avoidance, data acquisition and processing in a certain range, and connectivity with fog and cloud levels. To perform the safety and security analyses, the Hazard and Operability (HAZOP) and Threat and Operability (THROP) techniques are used. Based on the results obtained from them, the safety and security requirements are derived for the identification and prevention/mitigation of potential platooning hazards, threats and vulnerabilities. The assurance cases are constructed to show the acceptable safety and security of materials transportation using AGV platooning. We leveraged a simulation-based digital twin for performing the verification and validation as well as finetuning of the platooning strategy. Simulation data is gathered from digital twin to monitor platoon operations, identify unexpected or incorrect behaviour, evaluate the potential implications and trigger control actions to resolve them. The applicability of the AGV platooning is demonstrated in the context of a quarry site.

Safe and secure platooning of automated guided vehicles in Industry 4.0

Next generation embedded SoCs and next-generation autonomous systems application call for a revolution in the system software. Complexity and flexibility require hypervisor technology. Reliability, safety and determinism require advanced memory monitoring and control to avoid contention on caches and central RAM. In this talk, I will present the last years contributions towards building a future-proof industrially-demonstrated system software layer with open-source hypervisors.

The great responsibility of next-generation system software: Enabling determinism on high-performance embedded platforms

Middleware is an integral part of critical software, middleware provides core services for data exchange and manipulation, job execution, and scheduling. Their correctness is central to the correct execution of the software. They must be carefully configured to meet all functional and non-functional requirements. From a set of valid configuration parameters, one then has to demonstrate the implementation is correct and can fulfill its mission. Model-based techniques provide the foundations for correct-by-construction engineering. Most notably, they can be used to model a system, assess its configuration is correct, and then generate the corresponding middleware instance. The SAE AADL language supports the modeling of safety-critical systems and covers its design, configuration, and analysis. In this paper, we present several contributions: the definition of a model of computation aligned with the Ada Ravenscar profile supported by an architectural model expressed using the AADL; derivations rules from AADL constructs to middleware services using Ada 2012 and SPARK2014, and the proof of correctness of the implementation. Our contribution illustrates how one can prove the absence of runtime errors in middleware configured from high-level descriptions. This effort illustrates the positive effect models, programming languages and associated toolsets have on developing high-assurance middleware.

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A precompiler for memory model aware programming on higher language level

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Towards functional safety compliance of matrix-matrix multiplication for machine learning-based autonomous systems.

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